Meta-feature training models for machine learning algorithms
AssigneeFord Global Technologies, LLC
InventorHassan Fayez Amad
Life of this patent
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Abstract
A system and method are described herein for training and evaluating machine learning models trained using large complex datasets in an efficient manner. The systems enable methods for training a first model and generating predictions using the first trained model. The predictions and a portion of the data may be used to train a second model and generate predictions as an output of the second model. The first model may be used on a third dataset to generate predictions, which are appended to the third dataset. The appended third dataset is then input into the second model for generation of additional outputs that are compared against the third dataset for accuracy.
Claims20
A method for developing and training a neural network, the method comprising:
- training a first model using a first dataset;
- generating a first prediction for each of a plurality of first data using the first model;
- storing the first prediction for each of the plurality of first data with the first data in a second dataset;
- training a second model using the second dataset;
- generating a second prediction for each of a plurality of second data using the first model;
- storing the second prediction for each of the plurality of second data with the second data in a third dataset;
- and
- generating a final prediction for each item in the third dataset using the second model, wherein an accuracy of the final prediction is higher than an accuracy of the first prediction and the second prediction.
A non-transitory computer-readable medium having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to:
- train a first model using a first dataset;
- generate a first prediction for each of a plurality of first data using the first model;
- store the first prediction for each of the plurality of first data with the first data in a second dataset;
- train a second model using the second dataset;
- generate a second prediction for each of a plurality of second data using the first model;
- store the second prediction for each of the plurality of second data with the second data in a third dataset;
- and
- generate a final prediction for each item in the third dataset.
A system comprising:
- a processor;
- and
- a non-transitory computer-readable medium having instructions stored thereon that, when executed by the processor, cause the processor to:
- train a first model using a first dataset;
- generate a first prediction for each of a plurality of first data using the first model;
- store the first prediction for each of the plurality of first data with the first data in a second dataset;
- train a second model using the second dataset;
- generate a second prediction for each of a plurality of second data using the first model;
- store the second prediction for each of the plurality of second data with the second data in a third dataset;
- and
- generate a final prediction for each item in the third dataset.
Dependencies are inferred from the claim text.